We use the Fuzzy C-Means algorithm [1]} to assign a cost to the t-links.
The algorithm performs soft clustering and calculates the probability of a frame belonging to each cluster.
We subtract the probability value from 1 to obtain the cost of assigning a frame to each cluster.
The cost value for the n-links is assigned based on the temporal distance between the nodes.
For example, if the nodes are temporally closer (e.g., nodes at positions 1 and 2 in fig:graphcut), the cost of assigning the same label to them is lower, otherwise (e.g., for nodes at positions 1 and 5 in fig:graphcut), the cost is high.
After creating the graph \(G\) , we use \(\alpha \) -Expansion [2]} to find the minimum cost cut.
We use the discovered cut to assign frames to \(K\)  labels.
As shown in fig:graphcut, the lowest costs (highlighted in green) result in assigning the first and second frames to key-step 1, the third and fourth frames to key-step 2, and the last to key-step 3.
